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Mobile robots are widely used in the surveillance industry, for military and industrial applications. In order to carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning-based approach for semantic segmentation of stairs, behavioral cloning for stair alignment, and a novel mechanical design for an autonomous stair climbing robot. The main objective is to solve the problem of locomotion over staircases with the proposed implementation. Alignment of a robot with stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters and stair detection techniques. However, we could arrive at a more scalable and robust pipeline for alignment schemes. The proposed deep learning technique eliminates the need for manual tuning of parameters of the edge detector, the Hough accumulator and PID constants. The empirical results and architecture of stair alignment pipeline are demonstrated in this paper.

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Estimating the risk of collision with other road users is one of the most important modules to ensure safety in autonomous driving scenarios. In this paper, we propose new probabilistic models to obtain Stochastic Reachability Spaces for vehicles and pedestrians detected in the scene. We then exploit these probabilistic predictions of the roadusers' future positions, along with the expected ego-vehicle trajectory, to estimate the probability of collision risk in realtime. The proposed stochastic models only depend on the velocity, acceleration, tracked bounding box, and the class of the detected object. This information can easily be obtained through off-the-shelf 3D object detection frameworks. As a result, the proposed approach for collision risk estimation is widely applicable to a variety of autonomous vehicle platforms. To validate our approach, initially we test the stochastic motion prediction on the KITTI dataset. Further experiments in the CARLA simulator, by reproducing realistic collision scenarios, have the goal of demonstrating the effectiveness of the collision risk assessment and are compared with an alternative approach.